Navigating AI with pedagogical purpose: Toward an anatomy-centered framework for AI-enhanced learning

Area of Science:

• Medical Education
• Artificial Intelligence in Medicine
• Anatomy Pedagogy

Background:

• Artificial Intelligence (AI) is transforming medical education with personalized learning.
• Anatomy education, crucial for clinical practice, has unique visual, spatial, and foundational demands.
• Existing AI frameworks lack specificity for anatomy's pedagogical requirements, creating a critical gap.

Purpose of the Study:

• To identify and address the pedagogical gap in AI integration for anatomy education.
• To propose a novel framework, the Anatomy-AI Integration Framework, tailored for anatomy learning.
• To guide the intentional and clinically relevant integration of AI in anatomy.

Main Methods:

• Literature review of AI in medical education and clinical frameworks.
• Analysis of cognitive, psychomotor, and affective demands in anatomy education.
• Development of the Anatomy-AI Integration Framework with four key domains.

Main Results:

• The proposed Anatomy-AI Integration Framework offers a structured approach to AI implementation.
• The framework comprises Theoretical Grounding, Competency Alignment, Embodied Learning, and Ethical Engagement.
• A validation pathway is outlined for transitioning from a conceptual model to evidence-based practice.

Conclusions:

• The Anatomy-AI Integration Framework provides a much-needed pedagogical structure for AI in anatomy education.
• This framework ensures AI integration is pedagogically sound, clinically relevant, and addresses anatomy's specific learning needs.
• It facilitates the systematic and evidence-based application of AI technologies to enhance anatomy learning outcomes.